Single-Layer Perceptron Neural Networks
A single-layer perceptron network consists of one or more
artificial neurons in parallel. The neurons may be of the
same type we've seen in the Artificial Neuron Applet.
The perceptron learning rule, which we study next, provides a
simple algorithm for training a perceptron neural network. However,
as we will see, single-layer perceptron networks cannot learn
everything: they are not computationally complete. As
mentioned in the introduction, two-input networks cannot
approximate the XOR (or XNOR) functions. Of the
(22)n or 16 possible functions, a two-input
perceptron can only perform 14 functions. As the number of inputs,
n, increases, the proportion of functions that can be computed
- Each neuron in the layer provides one network output, and is
usually connected to all of the external (or environmental)
- The applet in this tutorial is an example of a single-neuron,
single-layer perceptron network, with just two inputs.
Later, we will investigate multilayer perceptrons.
[Back to the Simple Perceptron
Learning applet page ]